APPROACHES FOR BAYESIAN VARIABLE SELECTION
APPROACHES FOR BAYESIAN VARIABLE SELECTION
复制标题
DOI：
--
复制
发表时间：
1997-04
影响因子：
1.4
通讯作者：
E. George;R. McCulloch
中科院分区：
文献类型：
--
作者：
E. George;R. McCulloch
本文描述并比较了正态线性回归模型中变量选择不确定性的各种层次混合先验公式。其中包括 George 和 McCulloch (1993) 的非共轭 SSVS 公式，以及允许分析简化的共轭公式。讨论了基于实际意义的选择的超参数设置，以及使用具有点先验的混合的含义。考虑了事后评估和探索的计算方法。快速更新方法被认为提供了在中等规模问题中使用格雷码排序进行详尽评估以及在大型问题中使用快速马尔可夫链蒙特卡罗探索的可行方法。标准化常数的估计可以提供改进的个体模型概率和总访问概率的后验估计。在模拟样本问题和有关构建金融指数跟踪投资组合的实际问题上说明了各种程序。
This paper describes and compares various hierarchical mixture prior formulations of variable selection uncertainty in normal linear regression models. These include the nonconjugate SSVS formulation of George and McCulloch (1993), as well as conjugate formulations which allow for analytical simplification. Hyperpa- rameter settings which base selection on practical significance, and the implications of using mixtures with point priors are discussed. Computational methods for pos- terior evaluation and exploration are considered. Rapid updating methods are seen to provide feasible methods for exhaustive evaluation using Gray Code sequencing in moderately sized problems, and fast Markov Chain Monte Carlo exploration in large problems. Estimation of normalization constants is seen to provide improved posterior estimates of individual model probabilities and the total visited probabil- ity. Various procedures are illustrated on simulated sample problems and on a real problem concerning the construction of financial index tracking portfolios.